Quantitative Data Analyst (Financial Markets)

Role Overview We are looking for a Data Analyst with a strong quantitative background to extract actionable insights from raw stock market data. In this role, you wont just be building dashboards; you will be digging into massive datasetsranging from tick-by-tick price data to fundamental corporate metricsusing statistics, inference, and machine learning. The ideal candidate bridges the gap between data science and financial markets, possessing the coding rigor to clean messy financial data and the analytical mindset to discover market anomalies, alpha signals, or risk factors. Key Responsibilities Data Exploration & Engineering: Ingest, clean, and preprocess massive volumes of structured and unstructured financial data (e.g., price-volume series, order book data, corporate filings, macroeconomic indicators). Statistical Modeling & Inference: Apply rigorous statistical methods (time-series analysis, regression models, hypothesis testing) to validate market hypotheses and identify historical anomalies. Predictive Modeling: Design and prototype machine learning models to forecast market trends, price movements, or fundamental metrics. Feature Engineering: Extract and engineer high-quality features from raw price actions and financial statements to feed into quantitative strategies. Testing & Validation: Collaborate with stakeholders to test analytical insights and predictive models against historical data, ensuring robustness against overfitting. Insight Delivery: Standardize data pipelines and translate complex quantitative findings into clear, data-driven recommendations for the broader team. Required Skills & Qualifications Python: Proficiency in Python is mandatory. Working knowledge of the core data science stack: pandas/polars, numpy, scipy, and scikit-learn required. Database & SQL: Working knowledge of querying large relational databases and dealing with time-series databases (e.g., SQL, PostgreSQL, etc.). Quantitative Foundation: Solid grasp of probability, statistical inference, linear algebra, and time-series econometrics. Version Control: Clean Git hygiene and familiarity with collaborative workflows. Compensation & Other Details Stipend: 10,000 15,000 per month. Work Mode: In-Office Duration: 4 months. PPO Opportunity based on Performance Additional Perks: Free meals .